Project Entitled : “Health GIS and COVID-19: A Case Study of
West Bengal State, India”
REMOTE SENSING
Submitted By
Ujjwal Sahoo
West Bengal, India
Under the Guidance of
Dr. R.N.K. Sharma
Assistant Professor, Dept. of Remote Sensing, Birla Institute of
Technology, Mesra, 835215
DEPARTMENT OF REMOTE SENSING, BIRLA INSTITUTE OF
TECHNOLOGY, MESRA, JHARKHAND, INDIA, 2021
DECLARATION
I certify that
I.
The work contained in the thesis is original and has been done by myself under
the supervision of my supervisor.
II.
The work has not been submitted to any other Institute for any degree or diploma.
III.
I have conformed to the norms and guidelines given in the Ethical Code of
Conduct of the Institute.
IV.
Whenever I have used materials (data, theoretical analysis, and text) from other
sources, I have given due credit to them by citing them in the text of the thesis
and giving their details in the references.
V.
Whenever I have quoted written materials from other sources and due credit is
given to the sources by citing them.
………………………………………
Date : 30/07/2021 Name of the Student
Place : Paschim Medinipur
ACKNOWLEDGEMENTS
This project will not end successfully without the many people behind me
to move forward with unwavering direction, support and encouragement. I would
like to start by expressing my deep gratitude to my Birla Institute of Technology,
Mesra Supervisor for giving me the opportunity to work on this great project. He
taught and guided me in techniques to deeply acquire knowledge and develop
skills that were necessary in this research project. They guided me to sharpen my
skills to get the best out of me. I am my Birla Institute of Technology, Mesra
Supervisor Professor I would like to express my deep gratitude to Dr. R.N.K.
Sharma. He has consistently enlightened me with his great wisdom, the
completion of this project could not have been easier without his regular feedback
and guidance.
At last but not the least I would like to thank Father and Mother for their
immense love and support in all respect, for their continuous encouragement and
believing in me.
Title : Health GIS and COVID-19: A Case Study of West Bengal State, India
BY : UJJWAL SAHOO (WEST BENGAL, INDIA)
SUPERVISOR : DR. R.N.K. SHARMA (Birla Institute of Technology, Mesra)
ABSTRACT
The goal of this current study is to examine changes in NO2 and aerosols at different
times COVID19 epidemic phases. GIS is carving with the help of remote sensing The path
of mobile applications through which diseases are mapped and monitored Individuals are
possible. This seems to be due to the development of a vaccine against the virus Geo-
specific technology could be the overall solution towards COVID- 19 management.
Attempts have been made to explore this with the Covid-19 study. Field of study West
Bengal is a state where COVID-19 was available through data sites World and national
portals. ArcGIS 10.5 software is used for processing and Map generation. The study found
that Kavid-19 cases, deaths And recovery varies at the macro level. Geographical The
phenomenon can act as a potential controlling element. The current paper explores the
spatial pattern COVID-19 case and death in West Bengal (WB), India and Kolkata are its
source regions Content maps related to disease COVID in WB Issues are prepared with the
help of ArcGIS 10.5 Software. From 01/01/2020 to 29th June 2021, WB has 1498239 The
number of COVID-19 cases which is 9.03 Crore The total population of the state. 21 (50-
90%) District 74.11% of total population And carries 56.30% of the total COVID-19 cases.
However, the other two districts - Kolkata and North 24 Pargana The remaining 99% are
COVID-19 cases. Coefficient associated with COVID-19 Case and population density,
urban population and The concrete roof of their house is significant at 1% Level of
significance.
Keywords: COVID-19, West Bengal, Population Density, NO2,
Geospatial Technology
CONTENT
PAGE NO.
CHAPTER – I 6 - 9
1.
INTRODUCTION 6
1.1.
BENEFITS OF GEOGRAPHIC INFORMATION SYSTEM IN
HEALTHCARE 7
1.2.
OBJECTIVE 9
1.3.
RESEARCH QUESTIONS 9
CHAPTER – II 10 - 11
2.
STUDY AREA 10
CHAPTER – III 12 - 13
3.
MATERIALS (Data Used) 12
3.1.
COVID-19 12
3.2.
TREATMENT FOR COVID-19 12
3.3.
NO2 (Nitrogen Dioxide) 12
MATHODOLOGY 13
CHAPTER – IV 14 - 33
4.
RESULT AND DISCUSSION 14
4.1.
WEST BENGAL COVID-19 14
4.2.
RELATION BETWEEN COVID-19 AND POPULATION
DENSITY IN WEST BENGAL 18
4.3.
COVID-19 OF AIRPORT AREA IN WEST BENGAL 18
4.4.
RELATION BETWEEN NO2 AND COVID-19 21
4.5.
SPATIAL AUTOCORRELATION (MOREN’S I) 23
4.5.1. Output 24
5.
FUTURE SCOPE 31
6.
CONCLUSION 31
7.
Reference’s 32
Health GIS and COVID-19: A Case Study of West Bengal State, India
6
Chapter – I Introduction
1. INTRODUCTION :
The unimaginable potential of GIS to benefit the healthcare industry is now
beginning to be realized. Both the public and private sectors are developing innovative
ways to harness the power of GIS data integration and spatial visualization. Types of GIS
recipients for healthcare - from public health departments and public health policy and
research institutes to hospitals, medical centers and health insurance companies.
GIS plays an important role in determining where and when to intervene, improving
the quality of care, increasing service accessibility, exploring more affordable delivery
modes, and preserving patient privacy while meeting the research community's
requirements for data accessibility.
Public health uses of GIS include tracking child immunizations, conducting health
policy research, and establishing service areas and districts. GIS provides a way to move
data from the project level so that it can be used by the entire organization. Clinical and
administrative information can be disseminated in a visual and geographic manner that is
readily understood using ESRI Internet Map Server (IMS) technology. This health data can
be easily accessed using an Intranet or the Internet.
GIS or Geographic Information Systems
is a collection of various science and
technology tools used to manage geographic
relationships across space and to learn about
regions, to manage a project, to pick an ideal
site for something, and to combine different
types of information to make a choice. Delivery
route between things. It allows users to analyze locally referenced data and make decisions
about a wide range of issues, including business, economics and government applications
(Department of Health and Human Services).
Public health is a growing field that is leaning towards GIS for research applications.
For reference, public health is defined as a subject that studies the health of the population
Health GIS and COVID-19: A Case Study of West Bengal State, India
7
rather than individuals, focuses on treatment prevention, and usually works within the
government rather than private healthcare agencies. Does.
This article provides a brief history of its development as a tool in the field,
examines the use of GIS in public health by explaining how GIS is used and how sensitive
health information is kept confidential.
2. BENEFITS OF GEOGRAPHIC INFORMATION SYSTEM IN
HEALTHCARE:
I. Identifying Health Trends
The program assess the demographic data, such as home address, workplace, cancer
type, and even data collected from wearable health tech of all patients entered into the
system. Data is then georeferenced and mapped. Healthcare professionals can visualize the
locations of patients and determine if there are clusters of specific types of cancer
associated with similar working conditions or residential areas. One USC study found an
association between homes downwind of heavily sprayed fields and higher incidences of
prostate cancer.
With the number of chronic diseases such as cancer, diabetes, and cardiovascular
disease rising rapidly, GIS may provide a method in which healthcare professionals can
systematically address where certain diseases are more likely to or already have become
prevalent and begin proactively implementing preventative strategies or staffing healthcare
professionals skilled in specific medical specialties.
II. Tracking the Spread of Infectious Disease
The role of GIS systems should not be limited simply to tracking occurrences of
diseases though. One of its most powerful aspects is its ability to use geography and other
inputs to identify where diseases are most likely to spread next. Data such as this can be
essential to on-the-ground personnel working to save lives because it enables them to
prepare in advance for a disease and can severely limit the impact.
Health GIS and COVID-19: A Case Study of West Bengal State, India
8
III. Utilizing Personal Tech
Collecting a large amount of accurate personal data is expected to reveal a great deal
about personalized healthcare, but it can also greatly affect broader regional treatment
plans. Personal healthcare technologies present a powerful tool for data input to GIS due
to its ability to inform statistical studies.
Wearable technology is capable of collecting a very wide range of healthcare
information, such as average rates, sleep patterns, and exposure to the sun. Adding this data
to GIS can help determine if a person's average heart rate or sleep patterns change over a
geographic region. If such patterns are present, discovering why could open up new areas
of healthcare research.
IV. Incorporating Social Media
Just as wearable technology can be used to collect input data in the same way, social
media can also play a significant role. For example, words like COVID-19 and ‘drugs’
were used to predict where COVID-19 would have the greatest impact in predicting
additional studies and data collection.
V. Improving Services
Finally, the use of GIS technology could enable community leaders and developers to work
more closely with hospitals to take larger steps on national healthcare needs. The system
can help identify which neighborhoods need more specific health services, such as more
rehabilitation centers or senior care facilities. Analysis of patient population data can help
answer this question.
Health GIS and COVID-19: A Case Study of West Bengal State, India
9
3. OBJECTIVE
❖ Prepare COVID-19 map according to the population of West Bengal. Create Total
Cases, Death, Active and Recovery Map in every district of West Bengal.
❖ Comparing Total COVID-19 with Total NO2 in West Bengal.
❖ To prepare a map of Total COVID-19 in the area of the three airports in West
Bengal.
❖ Identifying of hot-spot and cold-spot for COVID-19 with Moran’s I.
4. RESEARCH QUESTIONS
I. Is there a relationship between COVID-19 diffusion for atmospheric NO2?
II. To what extend the NO2 affect the spread of COVID-19?
Health GIS and COVID-19: A Case Study of West Bengal State, India
10
Chapter – II About the study Area
2. STUDY AREA :
The eastern part of the state is West Bengal India has now been selected Study. This
state is located at 22.9868 ° N, 87.8550 ° E. The West Bengal government is publishing
Official bulletin and data of the daily COVID-19 Portal for public use from February 2020
and Research (https://www.wbhealth.gov.in/pages). West Bengal covers an area of about
6,7552 sq km, with a total population of 91,347,736. 2011 Census of India. It has an
average decade Population growth rate is 13.64 percent. A huge number of people there
(Including migrant labor) from West Bengal Working all over India and other countries.
Most They returned to the kingdom during the epidemic Period The return of people from
another state to the country And the countries are well recorded. The state has rescheduled
a specific start date for the COVID-19 infection. The state hall Darjeeling extends from the
foothills of the Himalayas Bay of Bengal to the north and south, and from the edge Higher
land up to the western boundary of Chhotanagpur Formerly Bangladesh and Assam.
Tropical, tropical, Humid, monsoon climate prevails in West Bengal. Natural vegetation
covers 13.93% of the total area west. Bengali. Leading profession in agriculture People of
West Bengal. Land of West Bengal Religion, culture, caste and its complex mix Language.
Health GIS and COVID-19: A Case Study of West Bengal State, India
11
Health GIS and COVID-19: A Case Study of West Bengal State, India
12
Chapter III Materials & Methods
3. Materials (Data Used):
3.1. COVID-19:
COVID-19 is a disease caused by a new strain of coronavirus. ‘CO’ stands for
corona, ‘VI’ for virus, and ‘D’ for disease. Formerly, this disease was referred to as ‘2019
novel coronavirus’ or ‘2019-nCoV.’ The COVID-19 virus is a new virus linked to the same
family of viruses as Severe Acute Respiratory Syndrome (SARS) and some types of
common cold.
3.2. TREATMENT FOR COVID-19:
At first there was no vaccine for COVID-19, now the vaccine is being given.
However, many of its symptoms can be treated And getting primary care from a healthcare
provider can make the disease less dangerous. There have been several clinical trials
conducted to evaluate the potential therapeutics of COVID-19.
3.3. NO2 (Nitrogen Dioxide):
Nitrogen dioxide or NO2 is a gaseous air pollutant composed of nitrogen and
oxygen and a group of related gases called nitrogen oxides or NO2. NO2 is formed when
fossil fuels such as coal, oil, gas or diesel burn at high temperatures. NO2 and other
nitrogen oxides in the outside air contribute to particle pollution and chemical reactions to
form ozone. It is one of the six broadest air pollutants that national air quality standards
have to limit their outdoor air. Fossil fuels such as wood or natural gas can also form NO2
indoors when burned.
• ARC GIS (10.5)
• MICROSOFT WORD
• EXCEL
Health GIS and COVID-19: A Case Study of West Bengal State, India
13
MATHODOLOGY
DATA
NON SPATIAL DATA
WEST BENGAL
SHAPEFILE
(DISTRICT WISE)
INDIA SHAPEFILE
SPATIAL DATA
NO2 DATA
CENSUS DATA
COVID-19 DATA
Health GIS and COVID-19: A Case Study of West Bengal State, India
14
Chapter – IV Result and Discussion
4. Result and Discussion
4.1. WEST BENGAL COVID-19
I downloaded COVID-19 Data and did the analysis using ArcGIS (10.5) software.
GIS is a software that can easily analyze any data in a short period of time. Paper and pencil
used to be needed to analyze any data, but now a system is needed that a GIS expert can
create a lot of maps in a very short time through computer or laptop.
On the West Bengal COVID-19 map we can see that the highest number of deaths
were in North 24 Parganas due to COVID-19, followed by COVID-19 in Kolkata,
Hooghly, Howrah, Nadia and South 24 Parganas. Jhargram, Kalimpong and Alipurduar
have the lowest, and the rest of the districts have more or less. So far (29/06/2021) Total
1498239 people have been infected and Total deaths have been 17676 people have been
newly infected 21116 people and 1459447 people have recovered. Below are given in the
form of tables.
Despite the current lockdown in West Bengal, COVID-19 is either going down or up
because the lockdown is not being followed properly.
WEST BENGAL COVID-19 DATA AT PRESENT (29/06/21)
Si. No.
District
Recoveries
Deaths
Active cases
1
Alipurduar
13696
98
369
2
Bankura
32145
257
823
3
Birbhum
39480
282
228
4
Cooch Behar
24682
90
1070
5
Dakshin Dinajpur
16190
167
224
6
Darjeeling
48270
455
1833
7
Hooghly
77871
869
1265
8
Howrah
90242
1465
1272
9
Jalpaiguri
36328
497
1305
10
Jhargram
9604
24
484
11
Kalimpong
5382
38
230
12
Kolkata
301347
4908
1701
13
Malda
32140
182
137
14
Murshidabad
32910
321
148
15
Nadia
65763
637
1071
Health GIS and COVID-19: A Case Study of West Bengal State, India
15
16
North 24 Parganas
308893
4469
2560
17
Paschim Bardhaman
54729
336
539
18
Paschim Medinipur
46521
463
1490
19
Purba Bardhaman
38130
173
584
20
Purba Medinipur
55928
358
2061
21
Purulia
18830
112
70
22
South 24 Parganas
91848
1246
1343
23
Uttar Dinajpur
18518
229
309
Total
1459447
17676
21116
Table No. - 01
Health GIS and COVID-19: A Case Study of West Bengal State, India
16
0
1000
2000
3000
4000
5000
6000
Total COVID
-19 Acitive and Rceoverie's
West Bengal All District's
Showing the Line Graph in Total COVID-19 Active and Death's in
West Bengal District's
Deaths Active cases
Health GIS and COVID-19: A Case Study of West Bengal State, India
17
Health GIS and COVID-19: A Case Study of West Bengal State, India
18
4.2. RELATION BETWEEN COVID-19 AND POPULATION DENSITY IN
WEST BENGAL
We can compare the population of West Bengal with the current COVID-19 in West
Bengal. In districts of West Bengal where the population is low and the area is large, the
rate of COVID-19 infection and recovery is low. But in districts where the population is
more but less than the size and population, the rate of COVID-19 is much higher. COVID-
19 more or less in proportion to the population size of different districts of West Bengal is
given below in the form of Table and Map.
4.3. COVID-19 OF AIRPORT AREA IN WEST BENGAL
West Bengal has two international and one national airport. The two international
airports are Netaji Subhash Chandra Bose International Airport Kolkata and Bagdogra
Airport Siliguri and the national airport is Durgapur, its name is Kazi Nazrul Islam Airport.
The worst affected area is Kolkata International Airport, which is shown in the form of a
buffer. The other two airport areas are more or less infected with COVID-19. The map
below shows that the most common COVID-19 infestation in the Kolkata airport area is
due to the fact that planes from different countries travel to the airport every day, so there
are a lot of people in the area, some traveling abroad for work or some traveling. Travel
for it. Below is a table and a map.
Health GIS and COVID-19: A Case Study of West Bengal State, India
19
Health GIS and COVID-19: A Case Study of West Bengal State, India
20
Health GIS and COVID-19: A Case Study of West Bengal State, India
21
4.4. RELATION BETWEEN NO2 AND COVID-19
It has been demonstrated that long-term exposure to air pollution is associated with
an increased prevalence of respiratory diseases and deaths. Fine particulate matter with size
<2.5 μm, PM2.5 is considered is one of the major health risk factors in the environment,
causing millions of deaths annually around the world. The presence of PM2.5 and another
one also PM10 are specifically associated with an increased rate of respiratory diseases,
and of hospitalization for chronic lung disease and pneumonia. Nitrogen dioxide (NO2) is
another important air pollutant that is toxic to human respiratory systems when present at
higher concentrations in the atmosphere It enters the atmosphere as a result of both
anthropogenic and natural processes. As the outdoor anthropogenic sources, NO2 is mainly
emitted from fuel combustion and transportation, in general, they come to the air from
vehicle exhaust gases and domestic heating. NO2 exerts adverse effects mainly on the
respiratory system, however, prolonged exposure to NO2 is correlated with a wide range
of severe illnesses such as hypertension, diabetes, and cardiovascular diseases and causes
even death. An early study also showed that chronic exposure to NO2 causes cytokine-
mediated inflammation in the lungs. Air pollution-related deaths include but are not
limited to bronchitis, aggravated asthma, respiratory allergies, heart disease, and stroke.
Exposure to air pollution especially NO2 and PM2.5 may increase the susceptibility
of infection and mortality from COVID-19. The available data also indicate that exposure
to air pollution may influence COVID-19 transmission. Moreover, air pollution can cause
adverse effects on the prognosis of patients affected by SARS-CoV-2 infection. The
available research findings on this topic may help the epidemiologists to select a proper
measure to prevent such an outbreak in the future. Attention should also be paid to the poor
communities, who are susceptible to be exposed to indoor air pollution, contributing to a
greater risk of becoming severely ill from COVID-19 infections. Air quality should be
counted as an important part of an integrated approach toward public health protection and
prevention to the spread of epidemics. Further research should be conducted focusing on
additional confounders such as age and pre-existing medical conditions along with
Health GIS and COVID-19: A Case Study of West Bengal State, India
22
prolonged exposure to NO2, PM2.5, and other air pollutants to confirm their detrimental
effects on mortalities from COVID-19.
Health GIS and COVID-19: A Case Study of West Bengal State, India
23
4.5. SPATIAL AUTOCORRELATION (MOREN’S I)
Moran’s I is a correlation coefficient that measures the overall spatial
autocorrelation of your data set. In other words, it measures how one object is similar to
others surrounding it. If objects are attracted (or repelled) by each other, it means that the
observations are not independent. This violates a basic assumption of statistics —
independence of data. In other words, the presence of autocorrelation renders most
statistical tests invalid, so it’s important to test for it. Moran’s I is one way to test for
autocorrelation.
Spatial autocorrelation is multi-directional and multi-dimensional, making it useful
for finding patterns in complicated data sets. It is similar to correlation coefficients, it has
a value from -1 to 1. However, while other coefficients measure perfect correlation to no
correlation, Moran’s is slightly different (due to the more complex, spatial calculations):
➢ -1 is perfect clustering of dissimilar values (you can also think of this as
perfect dispersion).
➢ 0 is no autocorrelation (perfect randomness.)
➢ +1 indicates perfect clustering of similar values (it’s the opposite of
dispersion)
Formula of Moran’s I
Health GIS and COVID-19: A Case Study of West Bengal State, India
24
The p-value is not
statistically significant.
You cannot reject the null hypothesis. It is quite possible that
the spatial distribution of feature values is the result of random
spatial processes. The observed spatial pattern of feature
values could very well be one of many, many possible versions
of complete spatial randomness (CSR).
The p-value is statistically
significant, and the z-score
is positive.
You may reject the null hypothesis. The spatial distribution of
high values and/or low values in the dataset is more spatially
clustered than would be expected if underlying spatial
processes were random.
The p-value is statistically
significant, and the z-score
is negative.
You may reject the null hypothesis. The spatial distribution of
high values and low values in the dataset is more spatially
dispersed than would be expected if underlying spatial
processes were random. A dispersed spatial pattern often
reflects some type of competitive process—a feature with a
high value repels other features with high values; similarly, a
feature with a low value repels other features with low values.
Table No. - 02
4.5.1. Output:
The Spatial Autocorrelation tool returns five values: the Moran's I Index, Expected
Index, Variance, z-score, and p-value. These values are written as messages at the bottom
of the Geoprocessing pane during tool execution and passed as derived output values for
potential use in models or scripts. You may access the messages by hovering over the
progress bar, clicking on the pop-out button, or expanding the messages section in the
Geoprocessing pane. You may also access the messages for a previously run tool via the
Geoprocessing History. Optionally, this tool will create an HTML report file with a
graphical summary of results. The path to the report will be included with the messages
summarizing the tool execution parameters. Clicking on that path will pop open the report
file.
Health GIS and COVID-19: A Case Study of West Bengal State, India
25
HOTSPOT AND CLODSPOT FOR COVID-19 WITH MORAN’S I IN
WEST BENGAL AT PRESENT (29/06/2021)
Table No. - 03
SI.
No.
District
Total
cases
Shape
Length
Shape
Area
GiZScore
GiPValue
Nneighbors
Gi_Bin
1
Alipurduar
14163
3.115198
0.267195
-0.888958
0.374026
3
0
2
Bankura
33225
4.355293
0.614052
-0.663807
0.506814
3
0
3
Birbhum
39990
5.443683
0.404091
-0.624301
0.53243
4
0
4
Cooch Behar
25842
6.271326
0.301111
-0.888958
0.374026
3
0
5
Dakshin
Dinajpur
16581
3.003507
0.187386
-0.965047
0.334521
3
0
6
Darjeeling
50558
2.861332
0.178818
-0.766151
0.443587
3
0
7
Hooghly
80005
4.090246
0.277391
1.745984
0.080814
4
1
8
Howrah
92979
2.37415
0.126432
3.007482
0.002634
7
3
9
Jalpaiguri
38130
3.089146
0.293094
-1.184235
0.23632
5
0
10
Jhargram
10112
4.003319
0.272451
-0.649491
0.516021
2
0
11
Kalimpong
5650
1.693861
0.106986
-0.766151
0.443587
3
0
12
Kolkata
307956
0.47333
0.008043
3.500173
0.000465
5
3
13
Malda
32459
4.155616
0.323374
-0.735969
0.46175
2
0
14
Murshidabad
33379
5.073042
0.478267
-0.515572
0.606153
2
0
15
Nadia
67471
5.442187
0.345079
-0.216725
0.828423
2
0
16
North 24
Parganas
315922
25.820816
0.509797
3.708837
0.000208
4
3
17
Paschim
Bardhaman
55604
2.269406
0.148505
-0.504808
0.613693
3
0
18
Paschim
Medinipur
48474
4.417576
0.55203
-0.341106
0.733024
4
0
19
Purba
Bardhaman
38887
4.760776
0.473617
-0.230386
0.817792
4
0
20
Purba
Medinipur
58347
4.364274
0.348275
0.226772
0.820601
4
0
21
Purulia
19012
5.147798
0.55218
-0.707007
0.479562
2
0
22
South 24
Parganas
94437
16.565523
0.483769
3.366143
0.000762
5
3
23
Uttar
Dinajpur
19056
5.483975
0.302708
-0.857387
0.391231
2
0
Health GIS and COVID-19: A Case Study of West Bengal State, India
26
-2
-1
0
1
2
3
4
Moran's i Test Positive and Negative
West Bengal All District's
Showing The Bar Graph of Total COVID-19 Cases With
Moran's I Test in West Bengal District's
GiZScore GiPValue
Health GIS and COVID-19: A Case Study of West Bengal State, India
27
Health GIS and COVID-19: A Case Study of West Bengal State, India
28
Health GIS and COVID-19: A Case Study of West Bengal State, India
29
Health GIS and COVID-19: A Case Study of West Bengal State, India
30
Spatial Autocorrelation Report
Global Moran's I Summary
Moran's Index:
0.443214
Expected Index:
-0.045455
Variance:
0.013772
z-score:
4.163997
p-value:
0.000031
Moran's Index: 0.443214
z-score: 4.163997
p-value: 0.000031
Given the z-score of 4.16399684149, there is a less than 1% likelihood that this clustered pattern could be
the result of random chance.
Health GIS and COVID-19: A Case Study of West Bengal State, India
31
Dataset Information
Input Feature Class:
WEST_BENGAL
Input Field:
2021-06-29 COVID-19.CSV.TOTAL
CASES
Conceptualization:
CONTIGUITY_EDGES_ONLY
Distance Method:
EUCLIDEAN
Row Standardization:
False
Distance Threshold:
None
Weights Matrix File:
None
Selection Set:
False
Table No. – 04
5. FUTURE SCOPE:
This study can be further carried out for third phase of COVID -19 has come so
it will be very interesting to take the data and do this study all over again to
understand and compare the difference between the first phase of COVID-19 and
the second phase of COVID -19. These maps show how many more people will be
infected with COVID-19 in the coming days. The number of vaccines in that ratio
will be known in advance. By looking at these maps more ordinary people can be
warned for COVID-19 in the coming days.
6. CONCLUSION :
The spatial distribution of the COVID-19 case is strongly Assam in West Bengal.
Calcutta is definitely the main one The origin of this disease is 1st in West Bengal The
Covid-19 case has started from Kolkata and to date the maximum number of patients and
deaths are increasing. Recorded here. We can say that understanding COVID-19 diseases
are much more common in big cities, Calcutta. Coefficients related to various factors
including Cavid-19 cases and deaths revealed that 'level' 'Urbanization', 'population
Health GIS and COVID-19: A Case Study of West Bengal State, India
32
density', 'concrete roof', And ‘distance from big city (Kolkata)’ is important The nature and
causes of the proliferation of COVID-19 Lawsuits and related deaths. We understand that
Cities are the most vulnerable to coronavirus Infection needs to be studied in detail to
understand the diversity of micro-levels within the city space Evaluate urban pattern, urban
space and its role Urban morphology on coronavirus infection. Preventive measures
‘lockdown’ or ‘quarantine’ shut down every possible economic activity. Therefore, we fear
that there will be poor people It is most important to be at risk for this disease The amazing
result is good housing conditions and COVID-19 cases and deaths are positively related.
The urban poor are the main source of labor 'Migrant workers' from rural areas. As a result
of the epidemic, there is a huge backlash in the rural areas Territories from the city center.
It was thought that Rural areas will suffer the most in Covid-1 cases.
The current study clearly indicates that the lockdown caused by the COVID 19 epidemic
has a direct impact on the environment. NO2 and aerosol and its reductions can be observed
with satellite images supplied by NASA Giovanni. The main reason for this decline is the
rules and regulations that are strictly followed by the people. All industrial activities are
shut down in this epidemic and it shows some of this terrible crisis and the positive and
power plants taken from NO2 from road transport. If we compare urban and rural areas, 19
cases of CVD are more common in metropolitan cities because the virus belongs to the
acute respiratory syndrome. According to researchers living with low air availability it may
be more susceptible to the disease.
7. Reference’s
https://www.ncbi.nlm.nih.gov/
https://www.aqi.in/dashboard/india/west-bengal
https://www.wbhealth.gov.in/pages/corona/bulletin
https://www.mohfw.gov.in/
https://www.who.int/emergencies/diseases/novel-coronavirus-2019
http://dataforall.org/dashboard/censusinfo/
https://censusindia.gov.in/2011-prov-results/prov_data_products_wb.html
https://www.sciencedirect.com/science/article/pii/S0160412020319942
Health GIS and COVID-19: A Case Study of West Bengal State, India
33
https://openknowledge.worldbank.org/handle/10986/33801
https://wb.gov.in/
https://en.wikipedia.org/wiki/West_Bengal
http://districts.nic.in/districts.php?sid=WB
https://giovanni.gsfc.nasa.gov/giovanni/
https://www.nature.com/articles/s41598-021-87673-2
http://www.cpcbenvis.nic.in/air_quality_data.html
https://www.aqi.in/dashboard/india/west-bengal